How to use from
OpenClaw
Start the llama.cpp server
# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf kingjux/ffmpeg-command-generator-gguf:F16
Configure OpenClaw
# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "kingjux/ffmpeg-command-generator-gguf:F16" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
Quick Links

FFMPEG Command Generator (GGUF)

Fine-tuned Qwen2.5-0.5B that generates FFMPEG commands from natural language with chain-of-thought reasoning.

Quick Start

LM Studio

lms import kingjux/ffmpeg-command-generator-gguf

Ollama

ollama run hf.co/kingjux/ffmpeg-command-generator-gguf

Example

Input: "Convert video.mp4 to webm format"

Output:

<think>
Task: Convert MP4 to WebM
- WebM uses VP9 video + Opus audio
- Use -c:v libvpx-vp9 for video
- Use -c:a libopus for audio
</think>

ffmpeg -i video.mp4 -c:v libvpx-vp9 -c:a libopus output.webm

Training

  • Base: Qwen2.5-0.5B-Instruct
  • Method: LoRA fine-tuning (r=16, alpha=32)
  • Dataset: 30 FFMPEG command examples with CoT reasoning
  • Trained on HuggingFace Jobs (T4 GPU)
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GGUF
Model size
0.5B params
Architecture
qwen2
Hardware compatibility
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